Performance and label efficiency of traditional deep-learning models and a retina-specific foundation model for ocular and systemic disease detection: a retrospective comparative study.

Authors

Samantha Min Er Yew, Xiaofeng Lei, Jocelyn Hui Lin Goh, Ke Zou, Yibing Chen, Sahana Srinivasan, Miao-Li Chee, Krithi Pushpanathan, Qingshan Hou, Zhi Da Soh, Can Can Xue, Marco Chak Yan Yu, Charumathi Sabanayagam, E Shyong Tai, Xueling Sim, Ya Xing Wang, Jost B Jonas, Vinay Nangia, Gabriel Dawei Yang, An Ran Ran, Carol Y Cheung, Yangqin Feng, Jun Zhou, Rick Siow Mong Goh, Yukun Zhou, Pearse A Keane, Yong Liu, Ching-Yu Cheng, Yih Chung Tham

Published in

The Lancet. Digital health. Pages 101031. Aug 28, 2026. Epub Aug 28, 2026.

Abstract

RETFound, a self-supervised retina-specific foundation model, has shown potential in downstream tasks, but its performance in comparison with that of traditional deep-learning models remains unclear. We aimed to evaluate RETFound against three supervised deep-learning models (ResNet50, ViT-Base, and SwinV2) pretrained on images from ImageNet for the detection of several ocular (disease-related visual impairment, visually significant cataract, glaucoma, and diabetic retinopathy) and systemic (diabetes, hypertension, and chronic kidney disease) diseases.
In this retrospective comparative study, all models were fine-tuned on different proportions of the training dataset (100%, 50%, and 20%) and on smaller datasets of fixed sizes (100, 200, and 400 images; 250 and 500 images for diabetic retinopathy) for all tasks. The fine-tuned models were tested on internal datasets from the Singapore Epidemiology of Eye Disease (SEED) study (1842-10 474 images) for all diseases except diabetic retinopathy, for which the Asia Pacific Tele-Ophthalmology Society 2019 dataset (1100 images) was used. Each model was also evaluated on external datasets, comprising population-based datasets (from the Beijing Eye Study, the Central India Eye and Medical Study, the Singapore Prospective Study, and the UK Biobank) and open-source datasets (Ocular Disease Recognition-5K [ODIR-5K], PAPILA, Glaucoma Grading from Multi-Modality Images [GAMMA], Indian Diabetic Retinopathy Image Dataset, and Methods to Evaluate Segmentation and Indexing Techniques in the Field of Retinal Ophthalmology-2). The performance of the models was assessed using 2000 pairwise bootstrap iterations of the area under the receiver operating characteristic curve (AUC) and compared using a two-tailed test with Bonferroni correction, with statistical significance defined as p<0·017 to account for the three pairwise comparisons between models.
In internal testing, similar performance was observed for traditional models (AUC 0·914 [95% CI 0·899-0·928] to 0·965 [0·952-0·976]) and RETFound (0·938 [0·920-0·954] to 0·966 [0·951-0·978]) in the detection of ocular disease after fine-tuning on full datasets. With smaller datasets, the performance of all models was similar, except in the case of diabetic retinopathy (≤100 images per class) and glaucoma (≤400 images), for which ResNet50 was inferior to RETFound (all p≤0·0001), although the performance of SwinV2 remained similar to that of RETFound. Similar patterns were observed with external test sets. As an example for glaucoma detection, after fine-tuning on 400 images, RETFound had an AUC of 0·908 (95% CI 0·888-0·928) when tested on the internal SEED dataset and, for the external datasets, AUCs of 0·835 (0·816-0·855) when tested on ODIR-5K, 0·779 (0·731-0·826) on PAPILA, and 0·990 (0·974-1·000) on GAMMA, performing significantly better than ResNet50 (SEED p=0·0001; ODIR-5k p<0·0001; PAPILA p=0·0032; and GAMMA p=0·0010). For systemic diseases, RETFound consistently outperformed traditional deep-learning models in internal testing when fine-tuned on smaller datasets (≤400 images): for example, for the detection of hypertension when fine-tuned on 100 images, the AUC for RETFound in the SEED dataset was 0·705 (0·687-0·723), compared with 0·634 (0·615-0·654) for ResNet50 and 0·648 (0·628-0·667) for SwinV2 (both p<0·0001) and 0·657 (0·637-0·676) for ViT-Base (p=0·0003). When tested in the external UKBB dataset, RETFound achieved an AUC of 0·622 (95% CI 0·618-0·625) for the detection of hypertension when fine-tuned on 400 images, 0·599 (0·595-0·602) on 200 images, and 0·599 (0·595-0·603) on 100 images, significantly outperforming ResNet50 and SwinV2 when fine-tuned on 400, 200, and 100 images (all p≤0·0001) and ViT-Base when fine-tuned on 400 images (p<0·0001) and on 200 images (p=0·0002).
Under this specific study design, the performance of traditional deep-learning models is similar to that of RETFound for ocular disease detection when fine-tuned on large datasets. By contrast, RETFound shows an advantage in the detection of systemic disease when fine-tuned on smaller datasets. These findings offer insights into the respective merits and limitations of traditional models and foundation models. Future benchmarking on broader datasets is warranted.
Agency for Science, Technology and Research (A∗STAR).

PMID:
42665469
Bibliographic data and abstract were imported from PubMed on 29 Aug 2026.

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